tests/testthat/_snaps/basics.md

In recipes: Preprocessing and Feature Engineering Steps for Modeling

Recipe fails on in-line functions

Code
  recipe(HHV ~ log(nitrogen), data = biomass)
Condition
  Error in `inline_check()`:
  ! No in-line functions should be used here; use steps to define baking actions.

Code
  recipe(HHV ~ (.)^2, data = biomass)
Condition
  Error in `inline_check()`:
  ! No in-line functions should be used here; use steps to define baking actions.

Code
  recipe(HHV ~ nitrogen + sulfur + nitrogen:sulfur, data = biomass)
Condition
  Error in `inline_check()`:
  ! No in-line functions should be used here; use steps to define baking actions.

Code
  recipe(HHV ~ nitrogen^2, data = biomass)
Condition
  Error in `inline_check()`:
  ! No in-line functions should be used here; use steps to define baking actions.

Using prepare

Code
  prepare(recipe(HHV ~ ., data = biomass), training = biomass)
Condition
  Error in `prepare()`:
  ! As of version 0.0.1.9006, used `prep` instead of `prepare`

bake without prep

Code
  bake(sp_signed, new_data = biomass_te)
Condition
  Error in `bake()`:
  ! At least one step has not been trained. Please run `prep`.

Code
  juice(sp_signed)
Condition
  Error in `juice()`:
  ! At least one step has not been trained. Please run `prep()`.

bake without newdata

Code
  bake(rec, newdata = biomass)
Condition
  Error in `bake()`:
  ! 'new_data' must be either a data frame or NULL. No value is not allowed.

tunable arguments at prep-time

Code
  recipe(Species ~ ., data = iris) %>% step_ns(all_predictors(), deg_free = .tune()) %>%
    prep()
Condition
  Error in `prep()`:
  ! You cannot `prep()` a tuneable recipe. Argument(s) with `tune()`: 'deg_free'. Do you want to use a tuning function such as `tune_grid()`?

logging

Code
  recipe(mpg ~ ., data = mtcars) %>% step_ns(disp, deg_free = 2, id = "splines!") %>%
    prep(log_changes = TRUE)
Output
  step_ns (splines!): 
   new (2): disp_ns_1, disp_ns_2
   removed (1): disp

Message

  -- Recipe ----------------------------------------------------------------------

  -- Inputs 
  Number of variables by role
  outcome:    1
  predictor: 10

  -- Training information 
  Training data contained 32 data points and no incomplete rows.

  -- Operations 
  * Natural splines on: disp | Trained

case weights are being infered correctly for formula interface

Code
  recipe(mpg ~ cyl + disp, data = mtcars2)
Condition
  Error in `too_many_case_weights()`:
  ! There should only be a single column with the role 'case_weights'. In these data, there are 2 columns.

case weights are being infered correctly for x interface

Code
  recipe(mtcars2)
Condition
  Error in `too_many_case_weights()`:
  ! There should only be a single column with the role 'case_weights'. In these data, there are 2 columns.

verbose when printing

Code
  tmp <- prep(standardized, verbose = TRUE)
Output
  oper 1 step center [training] 
  oper 2 step scale [training] 
  oper 3 step normalize [training] 
  The retained training set is ~ 0 Mb  in memory.

`internal data is kept as tibbles when prepping

Code
  bake(rec_prepped, new_data = as_tibble(mtcars))
Condition
  Error in `bake()`:
  ! bake() methods should always return tibbles

Code
  prep(rec_spec)
Condition
  Error in `prep()`:
  ! bake() methods should always return tibbles

recipe() errors if data is missing

Code
  recipe(mpg ~ .)
Condition
  Error in `recipe()`:
  ! Argument `data` is missing, with no default.

recipes documentation built on Aug. 26, 2023, 1:08 a.m.